Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.

Abstract: Embodiments enable a database management system (DBMS) to manage two levels of disk failure groups. These two levels of redundancy are achieved by grouping the disks of the disk group for the DBMS into two levels of failure groups (i.e., “data sites” each containing two or more “failure groups” of disks). This system of disk grouping allows a DBMS to potentially tolerate the loss of both an entire first site and part of a second site. Such a DBMS uses a multi-level voting system, based on both failure group-level votes and site-level votes, to identify the current version of administrative data structures (ADS) that store key administrative data. In addition to data sites that store database data, the DBMS includes a quorum site with a single quorum failure group that stores a copy of the ADS. The quorum site contributes a site-level vote during a multi-level voting event.

Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.

Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.

Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.

Abstract: Embodiments enable a database management system (DBMS) to manage two levels of disk failure groups. These two levels of redundancy are achieved by grouping the disks of the disk group for the DBMS into two levels of failure groups (i.e., “data sites” each containing two or more “failure groups” of disks). This system of disk grouping allows a DBMS to potentially tolerate the loss of both an entire first site and part of a second site. Such a DBMS uses a multi-level voting system, based on both failure group-level votes and site-level votes, to identify the current version of administrative data structures (ADS) that store key administrative data. In addition to data sites that store database data, the DBMS includes a quorum site with a single quorum failure group that stores a copy of the ADS. The quorum site contributes a site-level vote during a multi-level voting event.

Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.